A Survey on the MT Methods for Indian Languages: MT Challenges, Availability, and Production of Parallel Corpora, Government Policies and Research Directions

Sudeshna Sani, Samudra Vijaya, Suryakanth V Gangashetty
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Abstract

: Since 1991, machine translation has been a prominent research area in India, with IIT Kanpur pioneering the original work which has since been expanded to several universities. Only 10 percent of India’s 1.3 billion inhabitants can read, write, and speak English with varying degrees of competence, which makes machine translation crucial in overcoming the linguistic barrier to the internet. The Indian market for commercial products and events is greatly influenced by local languages, making the development and translation of region-based content an essential research topic nowadays. However, Indic-to-Indic language direct translation has faced several challenges and is still going through the experimental phase. Several government-sponsored projects are being undertaken in this regard. Still, there are limited sentence-aligned parallel bi-text resources available for the majority of Indian language pairs. This paper presents a detailed survey of the current trends of research on machine translation between Indian languages, along with their challenges over time. It also presents a timeline of recent research conducted and key findings of past surveys conducted over a decade. Under a single canopy, this paper provides sources of data, the progress made in developing datasets for low-resource Indian languages, various models of translation, encouragement from Indian Govt., and finally, new research directions.
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印度语言 MT 方法调查:MTC 的挑战、可用性和平行语料库的制作、政府政策和研究方向
:自 1991 年以来,机器翻译一直是印度的一个重要研究领域,印度理工学院坎普尔分校(IIT Kanpur)率先开展了这项原创性工作,后来又扩展到多所大学。印度有 13 亿人口,其中只有 10% 的人能够读、写、说不同程度的英语,因此机器翻译在克服互联网语言障碍方面至关重要。印度的商业产品和活动市场在很大程度上受到当地语言的影响,因此基于地区的内容开发和翻译成为当今必不可少的研究课题。然而,印度语到印度语的直接翻译面临着一些挑战,目前仍处于试验阶段。在这方面,有几个政府资助的项目正在进行中。然而,大多数印度语言对的句子对齐平行双文本资源仍然有限。本文详细介绍了当前印度语言之间机器翻译的研究趋势及其面临的挑战。本文还介绍了最近开展的研究的时间表以及过去十年间开展的调查的主要结果。在一个大标题下,本文介绍了数据来源、在开发低资源印度语言数据集方面取得的进展、各种翻译模型、印度政府的鼓励以及新的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Computing and Digital Systems
International Journal of Computing and Digital Systems Business, Management and Accounting-Management of Technology and Innovation
CiteScore
1.70
自引率
0.00%
发文量
111
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